1. Field of the Invention
The present invention relates to a method of vehicle segmentation and counting for night video frames, and more particularly, to a method of vehicle segmentation and counting utilizing the property of color variation and headlight information combining change detection in nighttime traffic environment.
2. Description of the Prior Art
Video object segmentation additionally considers the temporal information so it can process moving objects from video sequences. In video segmentation, indoor situation is more extensively discussed than the outdoor condition. However, the video surveillance system is the most common application in multimedia video, and it is unrealistic to deal only with the indoor condition. Outdoor circumstances can also be separated into daytime and nighttime conditions. Nighttime occupies almost half a day so that the video object segmentation in nighttime should be as important as daytime segmentation while most of the reported methods focus on daytime methods.
There are more affecting factors in outdoor circumstance than indoor condition, and even more obvious in nighttime. Normally, the streetlamps will affect the color of ground and produce the shadow of object. Also, they may make the reflection on the surface of static object at some angle and lead to erroneous segmentation of the moving object. Moreover, cars or bikes have bright headlights which are turned on to illuminate forward on the dark ground for driving (or riding) safety. Ground-illumination produced by headlights will be detected as moving object and deeply reduce the accuracy of object segmentation. Hence, outdoor video object segmentation in nighttime is a difficult task and most of the proposed methods can not obtain a satisfied result.
There are different methods to deal with outdoor-and-night condition in the prior art. Some methods handling the night surveillance sequence focus only on cars by processing the headlight pair information, and then exclude other regions in difference frame. Due to the high brightness of headlight, it is easy to get the headlight information. However, if the illumination on the ground appears to be a lamp because of the over-bright headlight, it will be detected as two cars (or bikes) while in fact there is only one. Furthermore, the above problem can't be overcome because it has lost the information of object body. Some methods use the far-infrared images to detect objects by measuring the thermal radiation. It can classify cars and pedestrians, but it may fail when the shape of object is asymmetric. Besides, it only uses the static image information but don't exploit the temporal information so it can not be employed to accomplish vehicle counting in traffic scenes.
Another problem in nighttime outdoor segmentation is the shadow effect. Methods in the prior art deal with shadow condition in daytime and obtain the satisfied result. However, most of the shadow detection methods focus on daytime environment without considering the following issues. First, at night shadows are produced by the streetlamps so that one object may have several shadows in different directions. Second, because the distance between object and streetlamp is much smaller than that between object and sky, the umbra region may be bigger than penumbra so that the previously mentioned methods handling penumbra region will fail. Due to these problems, shadow detection is a difficult task in night outdoor segmentation.